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4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

作者： Letouze, Patrick De Souza, J.I.M. Silva, Valeria Martins Climaco, Glaubos Chagas, F.B. Ishihara, Joao Y. Graduate Program of Computational Modeling of Systems Universidade Federal Do Tocantins Quadra 109 Norte Avenida NS-15 Palmas77001-090 Brazil Graduate Program of Systems' Engineering and Computation - PESC Universidade Federal Do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics at the Universidade Federal Do Maranh/ Ao - UFMA Brazil Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041794

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered;(2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible;(3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller Evolutionary Acquisition Interdisciplinary Research Project Management (MVC EA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines. © 2017 IEEE.

关键词： Clinical research

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High-Throughput Screening: One-Step Generation of a Drug-Releasing Hydrogel Microarray-On-A-Chip for Large-Scale Sequential Drug Combination Screening (Adv. Sci. 3/2019)

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Advanced science 2019年第3期6卷 1970014-1970014页

作者： Seo Woo Song Su Deok Kim Dong Yoon Oh Yongju Lee Amos Chungwon Lee Yunjin Jeong Hyung Jong Bae Daewon Lee Sumin Lee Jiyun Kim Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Seoul 08826 South Korea Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 08826 South Korea Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Nano Systems Institute Seoul National University Seoul 08826 South Korea Interdisciplinary Program in Bioengineering Seoul National University Seoul 08826 South Korea School of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 South Korea

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Applying Standard Independent Verification and Validation (IV&V) Techniques within an Agile Framework: Is there a Compatibility Issue?

Applying Standard Independent Verification and Validation (I...

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International systems Conference

作者： James B. Dabney James D. Arthur Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg Virginia Systems Engineering Program University of Houston - Clear Lake Houston Texas

ISBN: (纸本)9781509046249

Agile methods have gained wide acceptance over the past several years, to the point that they are now a standard management and execution approach for small-scale software development projects. While conventional Agile methods are not generally applicable to large multi-year and mission-critical systems, Agile hybrids are now being developed (such as SAFe) to exploit the productivity improvements of Agile while retaining the necessary process rigor and coordination needs of these projects. From the perspective of Independent Verification and Validation (IV&V), however, the adoption of these hybrid Agile frameworks is becoming problematic. Hence, we find it prudent to question the compatibility of conventional IV&V techniques with (hybrid) Agile practices. This paper documents our investigation of (a) relevant literature, (b) the modification and adoption of Agile frameworks to accommodate the development of large scale, mission critical systems, and (c) the compatibility of standard IV&V techniques within hybrid Agile development frameworks. Specific to the latter, we found that the IV&V methods employed within a hybrid Agile process can be divided into three groups: (1) early lifecycle IV&V techniques that are fully compatible with the hybrid lifecycles, (2) IV&V techniques that focus on tracing requirements, test objectives, etc. are somewhat incompatible, but can be tailored with a modest effort, and (3) IV&V techniques involving an assessment requiring artifact completeness that are simply not compatible with hybrid Agile processes, e.g., those that assume complete requirement specification early in the development lifecycle.

关键词： Independent Verification and Validation Agile

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Vibration-based classification of road damages: gyroscope data and a simple neural network model

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IOP Conference Series: Earth and Environmental science 2018年第1期195卷

作者： Fergyanto E. Gunawan Herriyandi Benfano Soewito Tuga Mauritsius Nico Surantha Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Framework for selecting the preferred networked computer system for dynamic continuous missions

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CrossTalk 2017年第2期30卷 17-21页

作者： Tolentino, Glenn Tian, Jeff Stracener, Jerrell Command and Control Department Space and Naval Warfare Systems Center Pacific San DiegoCA United States United States School of Computer Science Northwestern Polytechnical University Xi'an China Systems Engineering Program United States

This paper presents a framework for selecting a combination of existing systems to satisfy new, emerging requirements while reusing existing and proven capabilities to ensure mission success. Decision attributes will be considered during the selection process and will be used to measure the networked computer system's effectiveness to accomplish the mission. This approach will enable system stakeholders to make critical, well-informed decisions to address the continuing evolution of missions, threats, budget and technology.

关键词： Budget control

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Publisher Correction: Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

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Nature communications 2021年第1期12卷 4318页

作者： Bahar Behsaz Edna Bode Alexey Gurevich Yan-Ni Shi Florian Grundmann Deepa Acharya Andrés Mauricio Caraballo-Rodríguez Amina Bouslimani Morgan Panitchpakdi Annabell Linck Changhui Guan Julia Oh Pieter C Dorrestein Helge B Bode Pavel A Pevzner Hosein Mohimani Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Center for Microbiome Innovation University of California at San Diego La Jolla CA USA. Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA. Molecular Biotechnology Department of Biosciences Goethe University Frankfurt Frankfurt am Main Germany. Center for Algorithmic Biotechnology Institute of Translational Biomedicine St. Petersburg State University St Petersburg Russia. Tiny Earth Chemistry Hub University of Wisconsin-Madison Madison WI USA. Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. The Jackson Laboratory of Medical Genomics Farmington CT USA. Molecular Biotechnology Department of Biosciences Goethe University Frankfurt Frankfurt am Main Germany. helge.bode@mpi-marburg.mpg.de. Buchmann Institute for Molecular Life Sciences (BMLS) Goethe University Frankfurt & Senckenberg Research Institute Frankfurt am Main Germany. helge.bode@mpi-marburg.mpg.de. Max-Planck-Institute for Terrestrial Microbiology Department for Natural Products in Organismic Interactions Marburg Germany. helge.bode@mpi-marburg.mpg.de. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. ppevzner@ucsd.edu. Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA. hoseinm@andrew.cmu.edu.

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Tissue tropisms of avian influenza A viruses affect their spillovers from wild bird to domestic swine

Tissue tropisms of avian influenza A viruses affect their sp...

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作者： Xiaojian Zhang Fred L.Cunningham Lei Li Katie Hanson-Dorr Alicia Olivier Liyuan Liu Brandon S.Schmit Minhui Guan Jacqueline M.Nolting Andrew S.Bowman Mia Kim Torchetti Thomas J.DeLiberto Xiu-Feng Wan Missouri University Center for Research on Influenza Systems Biology (CRISB) University of Missouri Bond Life Sciences Center University of Missouri Department of Molecular Microbiology and Immunology School of Medicine University of Missouri Department of Basic Sciences College of Veterinary Medicine Mississippi State University Mississippi Field Station National Wildlife Research Center Wildlife Services Animal and Plant Health Inspection Service U.S. Department of Agriculture Department of Population and Pathobiology College of Veterinary Medicine Mississippi State University National Wildlife Disease Program Wildlife Services Animal and Plant Health Inspection Service U.S. Department of Agriculture Department of Veterinary Preventive Medicine The Ohio State University National Veterinary Services Laboratories Veterinary Services U.S. Department of Agriculture Department of Electrical Engineering & Computer Science College of Engineering University of Missouri Department of Pathobiology College of Veterinary Medicine University of Missouri MU Informatics Institute University of Missouri

Having both avian-like receptors (?±2,3-linked sialic acid, SA2,3Gal) and human-like receptors (?±2,6-linked sialic acid, SA2,6Gal), swine are proposed as??úmixing vessel??ùfor generating influenza pandemic strains. Laboratory experiments suggested all HA subtypes of influenza A virus (IAV) can infect swine. However, only sporadic cases of avian

关键词： Tissue tropisms of avian influenza A viruses affect their spillovers from wild bird to domestic swine

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